Occupancy detection using ambient sensors has many benefits such as saving energy and money, enhancing security monitoring systems, and maintaining the privacy. However, sensors data suffers from uncertainty and unreliability due to acquisition errors or incomplete knowledge. This paper presents a new heterogeneous sensors data fusion method for binary occupancy detection which detects whether the place is occupied or not. This method is based on using neutrosophic sets and sensors data correlations. By using neutrosophic sets, uncertain data can be handled. Using sensors data fusion, on the other hand, increases the reliability by depending on more than one sensor data. Accordingly, the results of experiments applied using Random Forest (RF), Linear Discriminant Analysis (LDA), and FUzzy GEnetic (FUGE) algorithms prove the new method to enhance detection accuracy.
Recently, a great interest has been dedicated to improving data fusion techniques for indoor occupancy detection. Indoor occupancy detection is extensively used in various applications, such as energy consumption control, surveillance systems, and disaster management. Using environmental sensors to collect data for detecting the occupancy state has the benefit of maintaining privacy. Also, it helps in improving monitoring systems and saving money due to energy consumption control. Nevertheless, sensor data is usually incomplete and noisy, which makes it uncertain and unreliable. These problems affect the detection accuracy. This paper proposes a comprehensive occupancy detection system that depends on a new fusion technique for fusing heterogeneous sensor data, which highly improves occupancy detection efficiency. Using Neutrosophy, the proposed technique handles sensor data uncertainty. Additionally, it improves reliability by fusing multiple sensor data. As it uses only one feature generated from fusing multiple sensors, data, training, and testing time are reduced. Consequently, the experimental results of applying the proposed fusion technique on a public benchmark dataset exhibit a significant enhancement in binary occupancy detection accuracy. The proposed technique enhanced the worst-case accuracy from 75.1 to 81.3%, 84.7 to 90.7%, 72 to 84.2%, 82.5 to 85.1%, and 65.9 to 78% using Linear Discriminant Analysis (LDA), K-Nearest Neighbors (K-NN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifiers, respectively. Using the other six performance metrics, the proposed technique results also outperform some state-of-the-art techniques.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.